1 Project Summary: Veterinary diagnostic laboratories currently lack universal standardized 2 methods and quality control for WGS data analysis with regard to assessing antimicrobial 3 resistance (AMR). As the FDA CVM is currently funding AMR monitoring and whole genome 4 sequencing (WGS) this is a significant gap. Additionally, AMR monitoring is lacking in minor 5 agricultural animal species and companion animals and should be included for holistic AMR 6 monitoring in veterinary medicine. This project addresses these gaps by developing data quality 7 criteria needed for AMR analysis using WGS, developing a bioinformatics pipeline with quality 8 assurance and quality control criteria tailored to veterinary diagnostic laboratory use and 9 providing AMR monitoring in minor agricultural animal species and companion animals. Not 10 only do resistant bacterial infections impact animal health and welfare, but they also have 11 significant potential for causing negative human health consequences through transmission of 12 resistant bacteria or resistance genes through food or animal contact. The use of common 13 classes of antimicrobials in humans and animals increases the likelihood that drug resistance 14 selected for in animal species could impact humans. Therefore, monitoring AMR in animals has 15 the potential to mitigate not only disease in animals but human infections as well. Recent 16 reductions in sequencing cost has provided an opportunity for veterinary diagnostic 17 laboratories to consider utilizing this technology for antimicrobial resistance assessment. 18 Escherichia coli, a microbe commonly harbored in multiple animal species will be used to 19 optimize and validate of existing bioinformatics platforms and bioinformatics quality 20 assurance/quality control procedures related to AMR. During the first year of funding the 21 laboratory will use reference strains of E. coli with known resistance phenotypes and genotypes 22 of interest including resistance to third generation cephalosporins, fluoroquinolones, 23 carbapenems, and aminoglycosides to optimize the sequence data quality assurance/quality 24 control and AMR bioinformatics analysis. In subsequent years, commensal E. coli will be 25 isolated from submissions to WADDL from multiple animal species for performance of 26 phenotypic AST and WGS for continuation of optimization and validation of the process, and 27 expansion of AMR monitoring in veterinary species. A summary of the data will be provided to 28 FDA CVM Vet-LIRN yearly and a standard operating procedure at the conclusion of the study. 29 30